Pattern recognition techniques, such as artificial neural networks are finding increased application in solving a variety of problems such as optical character recognition, voice recognition, and military target identification. In the automotive industry, pattern recognition techniques have now been applied to identify various objects within the passenger compartment of the vehicle, such as a rear facing child seat, as well as to identify threatening objects such as an approaching vehicle about to impact the side of the vehicle. See, for example, patent application Ser. No. 08/239,978 filed May 9, 1994 (now U.S. Pat. No. 5,563,462), Ser. No. 08/640,068 (now U.S. Pat. No. 5,829,782) and Ser. No. 08/247,760 (now abandoned) filed May 23, 1994 which are included herein by reference. Heretofore, pattern recognition techniques have not been applied to sensing automobile crashes for the purpose of determining whether or not to deploy an airbag or other passive restraint, or to tighten the seatbelts, cutoff the fuel system, or unlock the doors after the crash.
xe2x80x9cPattern recognitionxe2x80x9d as used herein means any system which processes a signal that is generated by an object, or is modified by interacting with an object, in order to determine which one of a set of classes the object belongs to. Such a system might determine only that the object is or is not a member of one specified class, or it might attempt to assign the object to one of a larger set of specified classes, or find that it is not a member of any of the classes in the set. The signals processed are generally electrical signals coming from transducers which are sensitive to either acceleration, or acoustic or electromagnetic radiation and, if electromagnetic, they can be either visible light, infrared, ultraviolet or radar.
To xe2x80x9cidentifyxe2x80x9d as used herein means to determine that the object belongs to a particular set or class. The class may be one containing all frontal impact airbag desired crashes, one containing all events where the airbag is not required, one containing all events requiring the passenger headrest to be moved into position, or one containing all events requiring the deployment of an airbag in the event of side impacts depending on the purpose of the system.
All electronic crash sensors currently used in sensing frontal impacts include accelerometers which detect and measure the vehicle accelerations during the crash. The accelerometer produces an analog signal proportional to the acceleration experienced by the accelerometer and hence the vehicle on which it is mounted. An analog to digital converter transforms this analog signal into a digital time series. Crash sensor designers study this digital acceleration data and derive therefrom computer algorithms that determine whether the acceleration data from a particular crash event warrants deployment of the airbag. This is usually a trial and error process wherein the engineer or crash sensor designer observes data from crashes where the airbag is desired and when it is not needed, and other events where the airbag is not needed. Finally, the engineer or crash sensor designer settles on an algorithm that seems to satisfy the requirements of the crash library, i.e., the crash data accumulated from numerous crashes and other events. The resulting algorithm is not universal and most such engineers or crash sensor designers will answer in the negative when asked whether their algorithm will work for all vehicles.
Several papers have been published pointing out some of the problems and limitations of electronic crash sensors which are mounted out of the crush zone of the vehicle, usually in a protected location in the passenger compartment of the vehicle, the crush zone being defined as that portion of the vehicle which has crushed at the time that the crash sensor must trigger deployment of the restraint system. These sensors are frequently called single point crash sensors. Technical papers which discuss these limitations along with discussions of the theory of crash sensing, which are relevant to this invention and which are included herein by reference, are:
1) Breed, D. S. and Castelli, V. xe2x80x9cProblems in Design and Engineering of Air Bag Systemsxe2x80x9d, Society of Automotive Engineers Paper SAE 880724, 1992.
2) Breed, D. S., Castelli, V. xe2x80x9cTrends in Sensing Frontal Impactxe2x80x9d, Society of Automotive Engineers Paper SAE 890750, 1989.
3) Breed, D. S., Sanders, W. T. and Castelli, V. xe2x80x9cA Critique of Single Point Crash Sensingxe2x80x9d, Society of Automotive Engineers Paper SAE 920124, 1992.
4) Breed, D. S., Sanders, W. T. and Castelli, V. xe2x80x9cA complete Frontal Crash Sensor System-I xe2x80x9d, Society of Automotive Engineers Paper SAE 930650, 1993.
5) Breed, D. S. and Sanders, W. T. xe2x80x9cUsing Vehicle Deformation to Sense Crashesxe2x80x9d, Presented at the International Body and Engineering Conference, Detroit Mich., 1993.
6) Breed, D. S., Sanders, W. T. and Castelli, V., xe2x80x9cA complete Frontal Crash Sensor System-IIxe2x80x9d, Proceedings Enhanced Safety of Vehicles Conference, Munich, 1994, Published by the US Department of Transportation, National Highway Traffic Safety Administration, Washington, D.C.
These papers demonstrate, among other things, that there is no known theory which allows an engineer to develop an algorithm for sensing crashes and selectively deploying the airbag except when the sensor is located in the crush zone of the vehicle. These papers show that, in general, there is insufficient information within the acceleration signal measured in the passenger compartment to sense all crashes. Another conclusion supported by these technical papers is that if an algorithm can be found which works for one vehicle, it will also work for all vehicles since it is possible to create any crash pulse in any vehicle. See in particular SAE paper 920124 referenced above.
In spite of the problems associated with finding the optimum crash sensor algorithm, many vehicles on the road today have electronic single point crash sensors. Some of the problems associated with single point sensors result in that an out-of-position occupant who is sufficiently close to the airbag at the time of deployment is likely to be injured or killed by the deployment itself Fortunately, systems are now being developed which monitor the location of occupants within the vehicle and can suppress deployment of the airbag if the occupant is more likely to be injured by the deployment then by the accident.
Since there is insufficient information in the acceleration data, as measured in the passenger compartment, to sense all crashes and since some of the failure modes of published single point sensor algorithms can be easily demonstrated using the techniques of crash and velocity scaling described in the above referenced technical papers, and moreover since the process by which engineers develop algorithms is based on trial and error, pattern recognition techniques such as neural network should be able to be used to create an algorithm based on training the system on a large number of crash and non-crash events which will be superior to all others. This in fact has proved to be true and is the subject of this invention.
Naturally, once any crash sensor has determined that an airbag should be deployed, the system should perform several other functions such as tightening the seatbelts for those vehicles which have seatbelt retractor systems, cutting off of the fuel system to prevent fuel spillage during or after the crash, and unlocking the doors after the crash to make it easier for the occupants to escape.
The use of pattern recognition techniques in crash sensors has another significant advantage in that it can share the same pattern recognition hardware and software as other systems in the vehicle. Pattern recognition techniques have proven to be effective in solving other problems related to airbag passive restraints. In particular, the identification of a rear-facing child seat located on the front passenger seat, so that the deployment of the airbag can be suppressed, has been demonstrated. Also, the use of pattern recognition techniques for the classification of vehicles about to impact the side of the subject vehicle for use in anticipatory side impact crash sensing shows great promise. Both of these pattern recognition systems, as well as others under development, can use the same computer system as the crash sensor of this invention. Moreover, both of these systems will need to interact with the main sensor and diagnostic module used for frontal impacts. It would be desirable for cost and reliability considerations, therefore, for all three systems to use the same computer system. This is particularly desirable since computers designed specially for solving pattern recognition problems, such as neural-computers, are now becoming available.
The present invention is a sensor and diagnostic module which uses pattern recognition techniques such as a neural network, or neural network derived algorithm, to analyze the digitized accelerometer data created during a crash, plus data from other sensors such as gyroscopes or angular rate sensors, velocity and position sensors, when available, to determine if and when a passive restraint such as an airbag should be deployed. Principal objects and advantages include:
1) To provide a single point sensor and diagnostic module including one or more accelerometers which makes maximum use of the information in the acceleration data to determine whether a passive restraint such as an airbag should be deployed.
2) To provide a universal single point crash sensor and diagnostic module which can be used on most automobiles without any modification required specific to each automobile.
3) To provide a single computer system which can perform several different pattern recognition functions within an automobile including, for example, crash sensing, identification of an object located within the vehicle passenger compartment, and the categorization of objects exterior to the vehicle.
4) To provide a sensor and diagnostic module comprising a crash sensor algorithm which is derived by training using a set of data derived from staged automobile crashes and non-crash events as well as other analytically derived data.
5) To provide a sensor and diagnostic module comprising a crash sensor based on pattern recognition techniques.
6) To provide a sensor and diagnostic module comprising a crash sensor which uses other data in addition to acceleration data derived from the crash wherein this data is combined with acceleration data and, using pattern recognition techniques, the need for deployment of a passive restraint is determined.
7) To provide a sensor and diagnostic module comprising a crash sensor which can be used for sensing both frontal, side, and rear impacts.
8) To provide a sensor and diagnostic module comprising a crash sensor which automatically retains the crash acceleration data from a period of time prior to the airbag deployment for later analysis.
9) To provide a sensor and diagnostic module comprising a crash sensor which uses a neural computer.
Other objects and advantages of this invention will become apparent from the disclosure which follows.
Generally, the present invention relates to a sensor and diagnostic module comprising a sensor system for initiating deployment of an occupant protection apparatus in a motor vehicle, such as an airbag, to protect an occupant of the vehicle in a crash. The system includes a sensor mounted to the vehicle for sensing accelerations of the vehicle and producing an analog signal representative thereof, an electronic converter for receiving the analog signal from the sensor and for converting the analog signal into a digital signal, a processor which receives the digital signal, a diagnostic system which determines that the sensor and its various components are functioning correctly and storage means for storing the state of the system at the time of a crash plus, in some cases, a time history of the data, including digitized accelerometer data, used by the system to determine whether or not the passive restraint should be deployed. The processor includes a pattern recognition system and produces a deployment signal when the pattern recognition system determines that the digital signal contains a pattern characteristic of a vehicle crash requiring occupant protection.
One embodiment of the method for initiating deployment of an occupant protection apparatus in accordance with the invention comprises the steps of sensing accelerations of the vehicle and producing an analog signal representative thereof, converting the analog signal into a digital signal, determining if the digital signal contains a pattern characteristic of a vehicle crash requiring occupant protection by means of pattern recognition means, or a single neural computer comprising pattern recognition.means, producing a deployment signal upon a determination by the pattern recognition means that the digital signal contains the pattern characteristic of a vehicle crash requiring occupant protection, and initiating deployment of the occupant protection apparatus in response to the deployment signal. The pattern recognition means comprise a single trained neural network such that the deployment signal is produced solely by the utilization of the single trained neural network. The digital signal may be derived from the integral of the analog signal. One or more sensors may be arranged to sense the accelerations of the vehicle and which are mounted in a position on the vehicle so as to sense frontal impacts and/or rear impacts. Additionally, data from other sensors such as gyroscopes or angular rate sensors, velocity and position sensors, may be used when available by the trained neural network. In certain advantageous embodiments, the method detects when the occupant to be protected by the deployable occupant protection apparatus is out-of-position and suppresses deployment of the occupant protection apparatus if the occupant is detected as being out-of-position. Similarly, the presence of a rear-facing child seat positioned on a passenger seat, i.e., and which would be affected by the occupant protection apparatus, can be detected and deployment of the occupant protection apparatus suppressed if a rear-facing child seat is detected.
Accelerations of the vehicle may be measured in two or more directions. In the embodiment wherein a neural computer is used, data from an anticipatory sensor and/or a collision avoidance sensor may be input into the single neural computer for consideration during the pattern recognition analysis.